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We propose in this paper a novel supervised manifold learning algorithm, called uncorrelated multilinear geometry preserving projections (UMGPP), incorporating both the Fisher criterion and manifold criterion to learn multiple interrelated subspaces in an iterative manner for efficient multimodal biometric recognition. In contrast to the existing GPP algorithm, UMGPP learns multiple feature subspaces...
A new approach is proposed to improve face recognition in the paper. The accurate face is detected by the position relation of the face and the eyes. The face features are extracted using the Gabor wavelet and the Adaboost algorithm is used to detect the face and the eyes. In the actual detection of the face, the face is probably inclining, and then we correct the detected face according to the positions...
Subspace methods have been successfully applied to face recognition tasks. It is well-studied in both unsupervised learning and supervised learning, such as Eigenface and Fisherface. In practice, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints is commonly available, which specifies whether a pair of instances belong to the same class or different classes....
In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterized, the proposed method focuses...
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